System and method for providing disposal recommendation for a vehicle owner

ABSTRACT

Systems and methods for providing a disposal recommendation for an owned vehicle are provided. The method includes generating a profile for the owned vehicle, the profile including age, mileage, and location of the owned vehicle, receiving monitoring data of the owned vehicle, and receiving sales data for non-owned vehicles corresponding to the profile. The method may further include generating predicted depreciation data of the owned vehicle, based on the sales data and the monitoring data, and providing a recommended disposal time for the owned vehicle, based on the predicted depreciation data.

TECHNICAL FIELD

The present invention generally relates to methods and systems forproviding financial recommendations, and, more particularly forproviding a recommendation for a disposal time for a user owning avehicle.

BACKGROUND

Vehicle depreciation is one of the largest costs of owning a vehicle fora user, with the vehicle maintenance costs coming in second. A vehicledepreciates due to wear and tear, as well as people's perception of avehicle. For example, a user may own a barely used Yugo GV, butdepreciation of such vehicle may be significant, due to people's lowappreciation of this vehicle.

The current approach for estimating depreciation for a vehicle isusually based on statistical parameters such as the age of the vehicle,mileage of the vehicle and vehicle type, make, model, and trim line. Inmany cases, however, the vehicles corresponding to the same statisticalparameters may be vastly different in their performance and outwardappearance resulting in significant variation in a selling price of thevehicles.

Accordingly, there is a need for providing systems and methods that canassist users owning vehicles in determining the value of their vehicles,and in providing a recommendation for when to sell their vehicles. Thedisclosed system and methods address the problems set forth above aswell as other deficiencies in existing systems and methods.

SUMMARY

Disclosed embodiments provide systems and methods for determining thevalue of a vehicle and for providing a recommendation for when to sell avehicle.

Consistent with a disclosed embodiment, a method for providing adisposal recommendation for an owned vehicle is provided. The method maycomprise generating a profile for the owned vehicle, the profilecomprising age, mileage, and location of the owned vehicle, receivingmonitoring data of the owned vehicle, and receiving sales data fornon-owned vehicles corresponding to the profile. The method may furthercomprise generating predicted depreciation data of the owned vehicle,based on the sales data and the monitoring data, and providing arecommended disposal time for the owned vehicle, based on the predicteddepreciation data.

Consistent with another disclosed embodiment, a method for providing adisposal recommendation for an owned vehicle is provided. The method maycomprise generating a profile for the owned vehicle, the profilecomprising age, mileage, and location of the owned vehicle, receivingmonitoring data of the owned vehicle, and receiving disposal data fornon-owned vehicles corresponding to the profile. The method may furthercomprise receiving wear-and-tear scores for the non-owned vehiclescorresponding to the profile, generating a wear-and-tear score for theowned vehicle based on the monitoring data, and providing a recommendeddisposal time for the owned vehicle, based on the received disposal datafor the non-owned vehicles, the wear-and-tear scores for the non-ownedvehicles, and the wear-and-tear score for the owned vehicle.

Consistent with another disclosed embodiment, a system for providing adisposal recommendation for an owned vehicle is provides. The system maycomprise a database configured to store a profile for the owned vehicle,the owned vehicle profile comprising age, mileage, and location of thefirst vehicle. The database may further be configured to storemonitoring data for the owned vehicle, and store sales data fornon-owned vehicles corresponding to the profile. The system may furthercomprise one or more memory devices storing instructions, and one ormore processors executing the instructions to perform operations thatmay include generating predicted depreciation data of the owned vehicle,based on the sales data and the monitoring data, and providing arecommended disposal time for the owned vehicle, based on the predicteddepreciation data.

Consistent with other disclosed embodiments, a memory device storinginstructions may store program instructions, which are executed by atleast one processor device and perform any of the methods describedherein.

The foregoing general description and the following detailed descriptionare exemplary and explanatory only and are not restrictive of theclaims.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are not necessarily to scale or exhaustive.Instead, the emphasis is generally placed upon illustrating theprinciples of the inventions described herein. These drawings, which areincorporated in and constitute a part of this specification, illustrateseveral embodiments consistent with the disclosure and, together withthe detailed description, serve to explain the principles of thedisclosure. In the drawings:

FIG. 1 is a diagram of an exemplary system for managing vehicle-relateddata consistent with disclosed embodiments.

FIG. 2 is a diagram of an exemplary vehicle monitoring system,consistent with disclosed embodiments.

FIG. 3 is a diagram showing details of the vehicle monitoring system ofFIG. 2 and an exemplary vehicle data system, consistent with disclosedembodiments.

FIG. 4 is a flowchart of an exemplary process of analyzingvehicle-related data, consistent with disclosed embodiments.

FIG. 5 is a chart illustrating vehicle price as a function of a vehicleage (time) or miles driven by a vehicle, consistent with disclosedembodiments.

FIG. 6 is a chart illustrating a number of vehicles as a function of avehicle price for a given vehicle age or miles driven by a vehicleconsistent with disclosed embodiments.

FIG. 7 is a diagram of an illustrative computer-based model forobtaining a vehicle wear-and-tear score using a monitoring data,consistent with disclosed embodiments.

FIG. 8 is a flowchart of an exemplary process of training computer-basedmodels, consistent with disclosed embodiments.

FIG. 9 is a graph of an exemplary relationship between vehicledepreciation and a wear-and-tear score for the vehicle, consistent withdisclosed embodiments.

FIG. 10 is a diagram of an exemplary computer-based model for obtaininga probability distribution of vehicle depreciation using wear-and-tearscore consistent with disclosed embodiments.

FIG. 11 is a diagram of an exemplary data record for training a machinelearning model.

FIG. 12 is a flowchart of an exemplary process of trainingcomputer-based models, consistent with disclosed embodiments.

FIG. 13 is a flowchart of an exemplary process of generating a list ofactions, consistent with disclosed embodiments.

FIG. 14 is a chart of exemplary actions leading to an increase in aneffective price of a vehicle, consistent with disclosed embodiments.

FIG. 15 is a chart of exemplary financial data that can be used todecide when to dispose of a vehicle, consistent with disclosedembodiments.

FIG. 16 is a chart of exemplary financial data that can be used todecide when to dispose of a vehicle, consistent with disclosedembodiments.

FIG. 17 is a chart of exemplary financial data that can be used todecide when to dispose of a vehicle, consistent with disclosedembodiments.

FIG. 18 is a graph of an exemplary change in vehicle price as a functionof vehicle age or miles traveled by a vehicle, consistent with disclosedembodiments.

FIG. 19 is a graph of an exemplary probability distribution for sellingvehicles as a function of a vehicle age consistent with disclosedembodiments.

FIG. 20 is a graph of an exemplary change in wear-and-tear score of avehicle as a function of vehicle age or miles traveled by a vehicle,consistent with disclosed embodiments.

DETAILED DESCRIPTION

Reference will now be made in detail to exemplary embodiments, discussedwith regards to the accompanying drawings. In some instances, the samereference numbers will be used throughout the drawings and the followingdescription to refer to the same or like parts. Unless otherwisedefined, technical and/or scientific terms have the meaning commonlyunderstood by one of ordinary skill in the art. The disclosedembodiments are described in sufficient detail to enable those skilledin the art to practice the disclosed embodiments. It is to be understoodthat other embodiments may be utilized and that changes may be madewithout departing from the scope of the disclosed embodiments. Thus, thematerials, methods, and examples are illustrative only and are notintended to be necessarily limiting.

The disclosed embodiments describe systems and methods for determiningthe vehicle value and for providing a recommendation for when to sell avehicle. As used herein, unless otherwise noted, the term “vehicle” mayinclude various types of vehicles. For example, a vehicle may include acar, a scooter, a bicycle, a motorcycle, a plane, a boat, a waterscooter, or the like. In some cases, the disclosed embodiments, whenapplicable to the discussion, may relate to systems and methods fordetermining the equipment value and for providing a recommendation whento sell the equipment. In some embodiments, the equipment may includevarious tools (e.g., power tools, etc.).

As used herein, unless otherwise noted, the term “depreciation” refersto a loss in a value of a vehicle relative to a price paid for the newvehicle. For example, a car that costs $30,000 when new, and $26,000after one year of use, the car depreciation is $4,000 or 14% loss fromthe original price. In the present disclosure, vehicle depreciation maybe defined either in terms of price loss (e.g., $4,000) or in terms ofpercentage loss (e.g., 14% loss from the original price). The term“price,” “current price” or “expected price” is the amount of money thatan inventory owner is expecting to get when selling the inventory.

The vehicle depreciation is one of the costs to a vehicle ownerassociated with owning a vehicle. Another cost is vehicle maintenance.Vehicle maintenance may include costs associated with repairing thevehicle, cleaning the vehicle, operating the vehicle or making paymentson the vehicle. The described vehicle maintenance costs are onlyillustrative, and other vehicle costs may be presented to the vehicleowner.

Vehicle maintenance may be closely related to the term “wear-and-tearscore” associated with the vehicle. The wear-and-tear score relates to acondition of the vehicle and is assigned to the vehicle based on variouspossible methods discussed further. In some embodiments, a wear-and-tearscore may include a single number, and in some cases, the wear-and-tearscore may contain a list of numbers (e.g., {Score 1, Score 2, . . .Score N}) detailing score for various aspects of the vehicle. Forexample, in some embodiments, a first score (Score 1) may be related toan outward appearance of the vehicle, while a second score (Score 2) maybe related to a condition of a mechanical component of the vehicle.

FIG. 1 shows a system 100 consistent with various embodiments of thepresent invention. In various embodiments, system 100 may be configuredto collect and process vehicle data, receive inquiries about the vehicledata from vehicle owners, and communicate data for the requestedinquiries to the vehicle owners. In some embodiments, system 100 may beassociated with a vehicle owner, and in some embodiments, system 100 maybe provided by a third party for vehicle owners.

System 100 includes a vehicle data system 105 coupled through a network115 to a vehicle owner 102. System 100 may further include sales data(i.e., data related to sales of various vehicles) 171, government (e.g.,Department of Motor Vehicles—DMV) data 174, external data 175, andvehicle history data 176. Sales data 171 may include data obtained fromcar dealers/car auctions 172 or the like, as well as data 173 fromfinancial institutions.

System 100 may allow vehicle data system 105 to communicate with network115 via a server 110, store data in a database 120, and processvehicle-related data via a data processing module 140. In variousembodiments, system 100 may communicate with vehicle owner 102 throughan interface 130. System 100 may include a computer-readable storagemedium that can retain and store program instructions for execution by aprocessor.

The computer-readable storage medium may be, for example, but is notlimited to, an electronic storage device, a magnetic storage device, anoptical storage device, an electromagnetic storage device, or anysuitable combination of the foregoing. A non-exhaustive list of morespecific examples of the computer-readable storage medium may include ahard disk, a random access memory (RAM), a read-only memory (ROM), anerasable programmable read-only memory (EPROM or Flash memory), a staticrandom access memory (SRAM), a portable compact disc read-only memory(CDROM), a digital versatile disk (DVD), a memory stick, or/and thelike.

Program instructions stored on a computer-readable storage medium mayinclude assembler instructions, machine dependent instructions, firmwareinstructions, source code or object code written in any combination ofone or more programming languages, including an object orientedprogramming languages, procedural programming languages or functionalprogramming languages. The programming language may be Fortran, Lisp,C++ or the like. The program instructions may be executed by a processorof the interaction system. In some embodiments, the program instructionsmay be executed by a processor of the user device, and in someembodiments, both the user device processor and the processor of theinteraction system may execute program instructions.

In various embodiments, vehicle data system 105 may receive sales data171 related to various vehicles via server 110 and store sales data 171in database 120. In some embodiments, sales data 171 may include cardealers' data 172. In some embodiments, car dealers may communicate data172 voluntarily, and, in some embodiments, car dealers' data 172 may beobtained by collecting information from a dealer's website which may beaccessed over network 115. Collected sales data 171 may include pricingdata, as well as data related to the year, make, model, trim line,mileage of the vehicle or the like. In some embodiments, sales data 171may be collected from polling companies, inventory management companiesor listing aggregators which may obtain and store inventory data fromone or more of dealers. Inventory polling companies, for example, aretypically commissioned by the dealer to pull car dealers data 172 andformat the data for use on websites; thus, inventory polling companiesmay contain all the necessary data 172 available from car dealers.

In various embodiments, vehicle data system 105 may receive financialinstitution data 173 related to sales data 171 for various vehicles.Financial institution data 173 may include data from entities such asbanks, credit union, etc. that provide any type of financial services toa participant involved in the purchase of a vehicle. For example, when abuyer purchases a vehicle, the buyer may utilize a loan from a financialinstitution, where the loan process usually requires two steps: applyingfor the loan and contracting the loan. These two steps may utilizevehicle and consumer information in order for the financial institutionto properly assess and understand the risk profile of the loan.Typically, both the loan application and loan agreement include proposedand actual sales prices of the vehicle. While vehicle data system 105may be implemented by a first party, in some embodiments of the presentinvention, system 105 may be associated with a financial institution.

In various embodiments, vehicle data system 105 may receive governmentdata 174 related to various vehicles. Government data 174 may beassociated with sales data 171. Government data 174 may include any datarelated to a vehicle. For example, when the vehicle is purchased, itmust be registered with the state (for example, DMV, Secretary of State,etc.) for tax and titling purposes. Government data 174 associated withsuch purchase typically includes vehicle attributes (for example, modelyear, make, model, mileage, etc.) and sales transaction prices for taxpurposes. As used herein, unless otherwise noted, the term “vehicleattributes” includes vehicle make, model, year, and trim line. Invarious embodiments, unless otherwise noted, when comparing vehicles,vehicles with the same vehicle attributes are compared.

In some embodiments, system 105 may receive external data 175 related tosales data 171 for various vehicles. External data 175 may comprisevarious other information sources, online or otherwise, which mayprovide other types of desired data, such as data regarding location ofvehicles, demographics at vehicle locations, current economicconditions, fuel prices, interest rates, and vehicle insurance ratesthat may influence current and future vehicle prices. In someembodiments, external data 175 may include data from manufacturers. Inorder to guide the pricing of their vehicles, the manufacturers mayprovide an invoice price and a manufacturer's suggested retail price(MSRP) for vehicles to be used as general guidelines for the dealer'svehicle price. These fixed prices may vary slightly by geographicregion. In various embodiments, external data 175 for vehicle datasystem 105 may include vehicle-related data collected from various usersdriving a variety of vehicles.

In various embodiments, system 105 may receive vehicle history reportssuch as vehicle history data 176. For example, vehicle history data 176may be obtained from services such as Carfax Inc. The vehicle historydata may include a vehicle year, make, model, trim line, overall vehiclecondition as determined by a party not interested in sale of the vehicle(e.g., a professional technician hired to evaluate vehicle condition),number of owners, accident history, service history, registrationhistory, open recalls and vehicle use (e.g., rental, fleet, personal).In some embodiments, history data for a vehicle may include historicaldata related to sales of the vehicle.

In some embodiments, vehicle data system 105 may obtain by gathering (orreceiving) sales data 171 and vehicle history data 176. This data mayinclude sales and historical data for a variety of vehicleconfigurations. Sales data 171 and vehicle history data 176 may beobtained at different time intervals, where the time interval utilizedin any particular embodiment for a certain type of data may be based, atleast in part, on how often that data is updated at the source, howoften new data of that type is generated, an agreement between thesource of the data and the providers of the vehicle data system 105 or awide variety of other factors.

In various embodiments, vehicle data system 105 may include a dataprocessing module 140, for data analysis and data manipulation. Forexample, data processing module 140 may evaluate if the obtained data isduplicative, falls within expected ranges, or/and conforms to expectedvalues. In some embodiments, module 140 may compare data for differentvehicles and match data that correspond to the same vehicle. In anillustrative embodiment, module 140 may obtain vehicle identifiableinformation (e.g., vehicle identification number (VIN)) and store allthe related information for the vehicle associated with that vehicleidentifiable information.

Vehicle data system 105 may interact with vehicle owner 102 operatingand owning a vehicle 103 via interface system 130. In variousembodiments, vehicle owner 102 may maintain a user account 150 withvehicle data system 105. User account 150 may maintain a vehicle profile151 as well as an information forum 152 for exchanging data andinformation with other owners. A vehicle profile is a set of datadescribing characteristics of the vehicle. In various embodiments,vehicle profile 151 may be used for maintaining data related to thevehicle and the owner.

In various embodiments, vehicle owner 102 may maintain a monitoringsystem 185 for monitoring the use of vehicles 103. It should be notedthat, in various embodiments, vehicle owner 102 may have one or morevehicles 103, and for each vehicle, owner 102 may maintain a separatemonitoring system 185.

In various embodiments, monitoring system 185 may include electronic andmechanical devices installed in vehicle 103 that measure various aspectsof vehicle operation such as, for example, vehicle acceleration anddeceleration (i.e., braking), vehicle speed, vehicle location, durationof a trip, mileage driven during a trip, angle and frequency of turningthe vehicle, speed of a vehicle during various vehicle turns, roadcondition based on vibration of the vehicle and/or the like. In anillustrative embodiment, an electronic device may include a smartphone.

In an example embodiment, various parameters other than the ones listedabove may be monitored by monitoring system 185. For example, system 185may monitor an oil-changing date, an oil-changing target due date, afilter-changing date, a belt-changing date, and various informationobtained from collecting onboard diagnostic data (OBD). The OBD data maybe collected, for example, by electronic devices, such as OBD scanners,and transmitted to vehicle owner system 190 wirelessly, via a wiredconnection, or via removable storage devices such as USB drives, memorycards, removable hard drives or the like.

In various embodiments, vehicle owner 102 may store vehicle-related datain vehicle profile 151. Vehicle profile 151 may use database 120 formaintaining the vehicle and owner related data. In some embodiments,vehicle profile 151 may include relevant information about vehicle 103such as vehicles' make, model, trim line, powertrain, options,geographical location, overall vehicle condition as determined by amechanic, vehicle history, such as service history, as well as vehicleexpected depreciation. As used herein, unless otherwise noted, the term“vehicle data” refers to any data that can be stored in vehicle profile151 or database 120. In various embodiments, data associated withvehicle 103 may be presented to vehicle owner 102 through graphs ortables via interface system 130.

In various embodiments, vehicle profile 151 may contain any vehicle datathat may be used by vehicle owner 102 to infer when to dispose ofvehicle 103. In some embodiments, vehicle profile 151 may contain linksto vehicle data for other vehicles that have the same vehicle attributesas the attributes of vehicle 103.

In various embodiments, data processing module 140 may be used toanalyze and manipulate data stored in vehicle profile 151 or in database120. For example, data processing module 140 may evaluate depreciationof vehicle 103 as well as an opportunity cost of maintaining vehicle103. In some embodiments, data processing module 140 may obtain datarelated to various vehicles from database 120 of vehicle data system 105and evaluate tradeoffs in opportunity costs for vehicle owner 102resulted from selling vehicle 103. In various embodiments, dataprocessing module 140 may suggest vehicle owner 102 alternative vehiclesthat may provide owner 102 more satisfaction than the currently ownedvehicle. The data obtained by data processing module 140 throughanalysis of data in vehicle profile 151 and database 120 may bepresented to vehicle owner 102 in a variety of ways via interface 130,such as through bar graphs, charts, tables, web pages or the like. Invarious embodiments, interface 130 may include, for example, a set ofinteractive web pages provided by vehicle data system 105.

In various embodiments, vehicle owner 102 may use interface 130 ofsystem 105 to obtain various related information about other vehiclesthat vehicle owner may consider purchasing. For example, using vehicledata system 105, vehicle owner 102 may specify a vehicle configurationby defining values for vehicle attributes (make, model, trim line,powertrain, options, etc.) and/or other relevant information such as ageographical location. Information associated with the specified vehicleconfiguration may include a price of the vehicle and the expectedvehicle depreciation. This information may then be presented to vehicleowner 102 through interface 130.

In some embodiments, vehicle owner 102 may maintain monitoring system185 that may include multiple components that are shown for example inFIG. 2. In some embodiments, system 185 may include an internal vehiclemonitoring system 210, an external vehicle monitoring system 220, and amonitoring interface system 230.

Internal vehicle monitoring system 210 may be used to monitor internalparameters of vehicle 103 such as OBD data collected with an OBDscanner. This monitoring data may include reporting faulty sensors(e.g., mass air flow sensor is faulty), low fuel pressure that can berelated to dirty fuel filter, exhaust leak between the first oxygensensor and engine, faulty oxygen sensor, misfire due to plugged fuelinjector, catalytic converter failure, transmission range sensor circuitmalfunction, or the like. In some embodiments, additional internalparameters may include failure of a timing belt that may be indicated bycheck engine light, brake failure (e.g., braking light is on), or oddnoises generated by vehicle 103. Internal vehicle monitoring system 210may include various sensors and scanners attached to a vehicle, such asOBD scanner, microphones positioned at various locations in the vehicleand connected to an audio recording device, temperature sensors, gassensors, video cameras positioned at various locations in vehicle 103and connected to video recording devices, or the like. In someembodiments, the sensors may include accelerometers and gyroscopes thatmay provide data to a data storage unit that may be associated withsystem 210. In some embodiments, system 210 may further include a mobiledevice such as a smartphone for monitoring the vehicle location, speed,changes in the vehicle direction (e.g., cornering), as well asacceleration and decelerating of the vehicle.

In various embodiments, monitoring system 185 may also include anexternal vehicle monitoring system 220. System 220 may include one ormore microphones, one or more cameras and/or one or more sensors formonitoring a vehicle interior and a vehicle exterior condition. Forexample, system 220 may include a camera that observes the maintenanceof the vehicle interior. In some embodiments, exterior system 220 mayinclude cameras that observe the vehicle exterior, such as the presenceof dents and scratches on the vehicle exterior. In some embodiments,system 220 may include sensors that may detect a vehicle collision withother vehicles or objects. For example, system 220 may include a sensorthat indicates that the vehicle has been hit from the back of thevehicle, from the front of the vehicle, from the side of the vehicle orthe like. In some embodiments, the sensors associated with system 220may detect that the vehicle has been impacted at a particular location.For example, sensors of system 220 may indicate that vehicle 103 hasbeen impacted by an opening door of another vehicle. In someembodiments, the sensors may correlate the accelerometer data with thevisual/sound data observed from a set of cameras or microphones, or insome embodiments, correlate the accelerometer data with available datarelated to the location of the vehicle. In an illustrative embodiment,sensors may detect the presence of a bump on a road and correlate thevisual data of the bump with a vertical vibration of the vehicle as itpasses over the bump. In addition, the sensors may correlate thevertical vibration with the speed of the vehicle while the vehicle ispassing over the bump. In some embodiments, the presence of the bump maybe obtained from the external data (e.g., data associated with the GPSlocation of the vehicle) that can be received by system 220.

In various embodiments, system 220 may also monitor weather condition atthe location of the vehicle. For example, system 220 may includetemperature sensors, air pressure sensors, wind sensors, humiditysensors, air quality sensors, salinity sensors (e.g., for detecting apresence of salt on a road), dust sensors, road condition sensors (e.g.,for detecting a presence of bumps, water, dirt on a road), radiationsensors (e.g., for detecting an ultraviolet radiation), ozone sensors,or the like. As used herein, unless otherwise noted, the term“monitoring data” refers to any type of data that can be monitored bymonitoring system 185.

In various embodiments, systems 210 and 220 may store the monitoringdata in an associated data storage unit, and transmit the monitoringdata to vehicle profile 151 of the user account 150. In someembodiments, the monitoring data may also be transmitted via a wirelessor a mobile network, or via a dongle that may be connected to the datastorage unit using a USB port. In some embodiments, the monitoring datamay be transmitted via a mobile network at a predetermined frequency,and in some cases, data may be transmitted via the mobile network when asignal strength for the network is above a threshold value. For example,the monitoring data may be transmitted when the mobile networkcorresponds to a signal strength of the LTE connection. In someembodiments, the monitoring data may be transmitted after a significantvehicle-related event. For example, the monitoring data may betransmitted when a vehicle is hit, when the vehicle is operated outsidestandard vehicle operating regulations (e.g., the vehicle is speeding,the vehicle is involved in a collision, the vehicle reports a highdeceleration or acceleration, or when the vehicle has faulty mechanicalor electrical components, such as faulty brakes). When vehicle 103 isoperated outside standard vehicle operating regulations, the monitoringdata may not only be transmitted to vehicle profile 151, but alsodirectly notify vehicle owner 102, or the monitoring data may berecorded in vehicle profile 151 and flagged for further analysis.

While the present discussion focuses on vehicles for transportation,such as cars, it is noted that other vehicles or other inventory mayhave monitoring functions that are specific to that type of inventory.For example, for the inventory including construction machinery, theexternal vehicle monitoring system 220 may include sensors that measurethe amount of weight carried or lifted by the construction machinery,the machinery traction with the ground, the reaction force received bymachinery during excavation, lifting, and drilling processes. In variousembodiments, internal vehicle monitoring system 210 may receive datarelated to hydraulic pumps, and various mechanical components that maybe unique to the machinery. In various embodiments, monitoring system185 can be used for boats, airplanes, trains, bicycles, motorcycles orthe like, with systems 210 and 220 tailored for a monitored vehicle. Forexample, when monitoring boats, monitoring system 185 may includesensors associated with leaks, humidity sensors, and sensors associatedwith surfaces of a hull of a boat to monitor the presence of growth(e.g., algae, barnacles, etc.)

Monitoring system 185 may not only monitor various aspects relating tovehicle 103 but may also be configured to monitor and record dataassociated with owner 102. In an example embodiment, monitoring system185 may monitor possible impairment of owner 102. For instance,monitoring system 185 may detect if owner 102 is driving under theinfluence, or is unusually agitated. In some instances, when selected byowner 102, monitoring system 185 may monitor movements of user owner102, owner's emotional state (e.g., monitoring system 185 may monitoremotional state via video camera or audio recording), as well as actionsof passengers in vehicle 103.

FIG. 2 shows that monitoring system 185 may include a monitoringinterface system 230 associated with vehicle owner 102. Interface system230 may allow owner 102 to monitor various parameters of vehicle 103,such as vehicle speed, location, vehicle mechanical/electricalcondition, acceleration, etc. In some embodiments, interface system 230may allow owner 102 to monitor vehicle in real time, and in someinstances, monitoring system 230 may alarm owner 180 when vehicle 103 isoperated outside standard vehicle operating regulations, or when vehicleexperience mechanical/electrical failure or vehicle collision.

In various embodiments, interface system 230 may include a screen thatdisplays various parameters associated with vehicle 103. In someembodiments, owner 102 may select the parameters to be displayed on thescreen by interacting with a screen through a touchscreen or through aset of user inputs, such as buttons. In some embodiments, interfacesystem 230 may include a software application installed on a user mobiledevice such as a smartphone for interacting with monitoring system 185,as shown for example in FIG. 3. In some cases, vehicle monitoring system185 may include a smartphone as a part of system 185 in communicationwith vehicle profile 151 of vehicle data system 105 as shown in FIG. 3.

In various embodiments, people related to owner 102 may be allowed toaccess user account 150. For example, user account 150 may have a firstset of permissions associated with user account 150 being accessed byowner 102, and may have a second set of permissions associated with useraccount being accessed by people related to owner 102. For example,owner 102 may be allowed to change data in vehicle profile 151 of useraccount 150, and people related to owner 102 may be allowed toread/observe data in user account 150. In an illustrated embodiment,people related to owner 102 may include relatives or friends of owner102. In some embodiments, user account 150 may be configured to allowrelatives to interact with owner 102 through interface system 130. Forexample, a relative may inform owner 102 that vehicle 103 is beingoperated outside standard vehicle operating regulations, that vehicle103 is experiencing a failure, or that vehicle 103 requires maintenance.In some cases, a relative may track the location of owner 102. In someembodiments, owner 102 may interact with other relatives about variousaspects of operating vehicle 103 via interface system 130. For example,owner 102 may request information of vehicle clearance height, vehicleride height, vehicle acceptable load, or the like.

In various embodiments, streaming monitoring data may be uploaded tovehicle profile 151 as a part of the history of the monitoring datastored in vehicle profile 151. The streaming monitoring data andpreviously recorded monitoring data may be processed by data processingmodule 140. FIG. 4 shows an exemplary process 400 for analyzing thevehicle data. At step 410 of process 400, vehicle data associated withvehicle 103 may be retrieved. The vehicle data may include themonitoring data associated with vehicle 103, as well as repairsassociated with vehicle 103. In various embodiments, system 105 may beconfigured to store repair data for non-owned vehicles corresponding tothe profile associated with vehicle 103 (i.e., corresponding toattributes of vehicle 103) as a part of vehicle profile data. In variousembodiments, system 105 may be configured to incorporate informationabout possible repair data when providing the recommendation for sellingvehicle 103. In various embodiments, the repair data may be stored indatabase 120 and may be accessed from vehicle profile 151. In variousembodiments, the repair data may include a type of the repair, a laborcost of the repair, and a parts cost of the repair associated withrepairing vehicle 103. At step 420, data processing module 140 mayevaluate a vehicle wear-and-tear score. The wear-and-tear score may becalculated based on vehicle data such as vehicle monitoring data,vehicle age, vehicle mileage, as well as repairs associated with vehicle103.

In various embodiments, owners 102 may share the monitoring dataassociated with their vehicle 103 anonymously with all the users ofsystem 105. In an example embodiment, owner 102 may share the monitoringdata by anonymously uploading the monitoring data to database 120 forvehicle 103. When sharing the monitoring data, vehicle attributes forvehicle 103 that are not related to personally identifiable information(e.g., VIN, license plate, the name of the user, etc.) may also beshared. Vehicle owners 102 may share the monitoring data in order toobtain access to the monitoring data of other owners. Alternatively,system 105 may encourage sharing of the monitoring data by providingincentives to owners. For example, system 105 may perform a dataanalysis for owners 102 if owners 102 share their monitoring data withthe other owners. As data is shared anonymously, vehicle system 105 maynot be configured to share identities of owners 102 driving vehicles103. In various embodiments, vehicle system 105 may provide otherincentives and advertising for owners sharing the data in return for thevalue obtained from their monitoring data.

In various embodiments, owners 102 with user accounts 150 may share notonly the monitoring data but also the repair and sales data associatedwith vehicles 103. In an example embodiment, owners 102 may share thesales data of their vehicles, the repair data for their vehicles, thevehicles that they are interested in purchasing in the future, or/andthe updates to their vehicles. In various embodiments, vehicle system105 may be configured to poll owners 102 about various aspects of theirvehicles and provide them with various inputs from the other owners. Forexample, vehicle data system 105 may provide owners 102 a daily digestof the latest trends in vehicles related to vehicles that are similar tovehicle 103. In some embodiments, vehicle system 105 may providestatistical data for reliability and depreciation of vehicles withvehicle attributes similar to the ones of vehicle 103. As used herein,unless otherwise noted, the term “similar vehicle” may refer to avehicle with at least a year and a make, that is the same as the yearand the make of the other vehicle.

In an illustrative embodiment, data processing module 140 of vehicledata system 105 may be configured to calculate a wear-and-tear scorebased on depreciation of various vehicles 103 owned by owners 102 thatmaintain user account 150 with system 105. For example, vehicle datasystem 105 may maintain historical depreciation values for all vehicles103 maintained and disposed of by owners 102. To evaluate awear-and-tear score for a vehicle with given vehicle attributes, vehicleowner system 102 may retrieve depreciation values and vehicle data forall previously disposed of vehicles matching the given vehicleattributes. In an illustrative embodiments, the vehicle data (e.g.,vehicle monitoring data, vehicle age, vehicle mileage, vehicle repairs)associated with the vehicles with low depreciation values may result ina low corresponding wear-and-tear score, while the vehicle dataassociated with the vehicles with high depreciation values result inhigh wear-and-tear score, with low wear-and-tear score being a desirablescore.

FIG. 5 shows an example chart of depreciation of various vehicles as afunction of time (i.e., vehicle age) or miles traveled by the vehiclefor a vehicle with given vehicle attributes. The chart may be, forexample, presented by interface system 130 to owner 102. In anillustrated embodiment, graph 500 may be a graph of points (points in aregion 540 are illustrated). For example, the point associated withvehicle data VD1 corresponds to a vehicle sold at price P1 attime/millage T1, while point associated with vehicle data VD2corresponds to a vehicle sold at price P2 at time/millage T1. Sinceprice P1 is higher than price P2, the vehicle associated with vehicledata VD1 has a lower wear-and-tear score when compared to awear-and-tear score for the vehicle associated with vehicle data VD2. Invarious embodiments, the wear-and-tear score may be normalized resultingin the wear-and-tear score for vehicles with price Pmax being zero (thebest wear-and-tear score), and the wear-and-tear score for vehicles withprice Pmin being 100 (the worst wear-and-tear score). It should be notedthat prices Pmax and Pmin are obtained using a statistical approach. Forexample, FIG. 6 shows the distribution of cars for various vehicleprices as illustrated by vehicle data points in region 540. Inillustrative embodiments, the region 650 between point 520 correspondingto vehicles with high depreciation and point 510 corresponding tovehicles with low depreciation may include 95% of all the cars disposedof by owner 102 having time/millage T1 (as shown in FIG. 5). It shouldbe noted, that 95% value is chosen as an illustrative value only, andany suitable value may be used as well.

In various embodiments, vehicle data such as (e.g., vehicle monitoringdata, vehicle age, vehicle mileage, and vehicle repairs) for vehiclesdisposed of at a given price may vary. Thus, different vehicle data maycorrespond to the same wear-and-tear score based only on depreciationrates for the vehicle.

In various embodiments, the wear-and-tear score may be calculated invarious other ways. In an example embodiment, individual wear-and-tearscores may be calculated for various components of the vehicle, and acombined wear-and-tear score may include a set of individualwear-and-tear scores. For example, main vehicle components such asbrakes, electrical system, engine, transmission or the like may haveassociated wear-and-tear scores. In an illustrative embodiment, awear-and-tear score for a component for a vehicle with particularvehicle attributes may be calculated based on the age of the component,the vehicle mileage, and the monitoring data. In an example embodiment,a wear-and-tear score of a new component may be low (e.g., zero) and mayincrease during the usage of the vehicle. In an example embodiment,braking data for the vehicle may be used to establish a wear-and-tearscore for brakes. For example, a set of braking events (e.g., 1000braking events resulting in vehicle deceleration above a certainthreshold) may increase the wear-and-tear score associated with brakingby one unit. The above example of calculating a wear-and-tear score forbraking is only illustrative, and other approaches may be used.

Similar to a wear-and-tear score for brakes, a wear-and-tear score maybe established for other components. For example, a wear-and-tear scorefor an engine may, in part, be based on a regularity of an oil change.Similarly, a wear-and-tear score of various components may depend on theregularity of maintenance of these components. In some embodiments, thewear-and-tear score for a component may exhibit dramatic changes ifmonitoring system 185 detects unusual performance of the component. Forexample, if monitoring system 185 reports the failure of a givencomponent, the wear-and-tear score of such component may be maximum(e.g., 100).

It should be noted, that a wear-and-tear score may be calculated in manyother possible ways. In some cases, the wear-and-tear score may beevaluated by a certified technician. In some cases, the wear-and-tearscore may be obtained by averaging wear-and-tear score calculated byseveral different approaches. In some cases, the wear-and-tear score forthe entire vehicle can be obtained as a weighted average of individualwear-and-tear scores. In an example embodiment, the weight for anindividual wear-and-tear score may be selected based on the importanceof components associated with such individual wear-and-tear score.

In some embodiments, wear-and-tear score may be calculated by comparingthe monitoring data of vehicle 103 to the monitoring data of othervehicles maintained by system 105 that have wear-and-tear score computedfor those vehicles. In an example embodiment, data processing module 140may process monitoring data and obtain a set of important statisticalcumulative and average monitoring parameters such as cumulative vehiclebreaking, cumulative vehicle cornering, cumulative vehicle acceleration,cumulative mileage of the vehicle, average time between the vehiclemaintenance (e.g., average time between the oil change, average timebetween transmission fluid change, etc.) and average speed of thevehicle. It should be noted that examples of cumulative and averagemonitoring parameters described above are only illustrative and variousother parameters may be used.

FIG. 7 shows an exemplary embodiment of obtaining a wear-and-tear score730 for a vehicle using a model 720 for a monitoring data represented bya number of monitoring parameters 710 such as cumulative vehiclebreaking, cumulative vehicle cornering, cumulative vehicle accelerationor the like. In various embodiments, wear-and-tear score 730 may becalculated using model 720 that may include machine learning models,such as neural networks, decision trees, and models based on ensemblemethods, such as random forests. The machine learning models may haveparameters that may be selected for optimizing the performance of model720. For example, parameters specific to a particular type of model(e.g., the number of features and number of layers in a neural network)may be optimized to improve the model's performance. In some embodimentsmodel 720 may return a single number related to wear-and-tear score ofvehicle 103, and in some embodiments wear-and-tear score may include aset of individual wear and tear scores (e.g., Score 1 through Score N)as described above.

In various embodiments, model 720 may be trained using a data setcontaining monitoring parameters and wear-and-tear scores of variousother vehicles 103 of owners 102 with the associated user account 150.FIG. 8 illustrates an exemplary process 800, of training model 720 fordetermining wear-and-tear score 730 from monitoring parameters 710. At astep 810 of process 800, training data is selected for training amachine learning model, such as model 720. In various embodiments, thetraining data may be related to a vehicle with particular vehicleattributes. In various embodiments, the training data may includemileage for an associated vehicle in addition to monitoring parameters710, as well as related wear-and-tear score, that may be obtained forvehicle 103 using alternative approaches for evaluating wear-and-tearscore described above. In some embodiments, the training data may alsoinclude repairs done to a vehicle. In various embodiments, training datamay include multiple data records, with each record processedconsecutively by model 720. At step 810 of process 800, model 720 canacquire a training data record, at a step 820 perform computations, andat a step 830 may generate a predicted wear-and-tear score for thevehicle associated with the training data record. In variousembodiments, the predicted wear-and-tear score may be compared with aknown wear-and-tear score to evaluate an associated error for model 720at a step 840. If the error is below the threshold value (step 840, NO),process 800 may proceed to step 810 of acquiring a next training datarecord 801. If the error is above the threshold value (step 840, YES),process 800 may proceed to a step 850 of modifying model parameters andsubsequently returning to step 820. In various embodiments, model 720may be rated based on the average error generated by model 720. Invarious embodiments, a model may be tailored for each vehicle with givenvehicle attributes.

In various embodiments, the external condition of the vehicle andperformance of various options within a vehicle may significantly affectthe overall wear-and-tear score for the vehicle as the vehicle may notbe appealing to buyers; thus affecting the vehicle depreciation. Forexample, if a vehicle contains dents and stains in the vehicle interior,the wear-and-tear score for the vehicle may be increased. Thus, theexternal condition of the vehicle may be considered as one of thecomponents of a vehicle for calculating a wear-and-tear score. Invarious embodiments, the individual wear-and-tear score based only onexternal condition of the vehicle may be evaluated by consideringdepreciation of other vehicles having the same vehicle attributes andsimilar individual wear-and-tear scores for various vehicle componentsother than the external condition of a vehicle.

Returning to the flowchart of FIG. 4, at step 430, data processingmodule 140 may analyze a vehicle depreciation based on the wear-and-tearscore for vehicle 103 and at step 440, data processing module 140 maystore data including wear-and-tear score and the predicted depreciationfor vehicle 103 in database 120 and vehicle profile 151. The vehicledepreciation may be estimated at step 430 based on the correlationbetween the depreciation of previously disposed of vehicles and theirreported wear-and-tear scores.

In an illustrative example shown in FIG. 9, a wear-and-tear score andrelated depreciation of previously disposed of vehicles are plotted as aset of points 901. A linear regression line 910 may be plotted throughthe set of points, as shown in FIG. 9. Using a calculated wear-and-tearscore for vehicle 103 (e.g., S1) vehicle owner 102 may obtain theexpected depreciation of the vehicle D1 as shown in the FIG. 8 Invarious embodiments, as explained before, a wear-and-tear score for avehicle may be calculated based on vehicle data such as vehiclemonitoring data, a vehicle age, a vehicle mileage, as well as repairsassociated with vehicle 103.

FIG. 10 shows an exemplary process of obtaining a depreciation rate 1030for a vehicle using a model 1020 for a wear-and-tear score 1010represented by a number of individual wear-and-tear scores, Score 1through Score N. In various embodiments, depreciation rate 1030 may becalculated using model 1020 that may include machine-learning models,such as neural networks, decision trees, and models based on ensemblemethods, such as random forests. The machine-learning models may haveparameters that may be selected for optimizing the performance of model1020. For example, parameters specific to a particular type of model(e.g., the number of features and number of layers in a neural network)may be optimized to improve the model's performance. In someembodiments, as shown, for example, in FIG. 10 model 1020 may return acurrent price for vehicle 103 characterized by a single number asindicated by element 1025, and in some embodiments, as shown in FIG. 10,a model 1020 may return a probability distribution 1050 of possibleprices for vehicle 103.

In various embodiments, model 1020 may be trained using a data setcontaining information related to vehicle data, a wear-and-tear score ofa vehicle and a depreciation of previously disposed of vehicles. FIG. 11illustrates a data record 1101 for training a machine learning model.Data record 1101 may include a wear-and-tear score 1103 that may includea set of individual wear-and-tear scores (e.g., Score 1 through Score N)for a previously sold vehicle, together with vehicle depreciation data1105. FIG. 12 shows an example embodiment of process 1200, at a step1210, a training data is selected for training a machine learning model,such as model 1020. In various embodiments, the training data may berelated to a vehicle with particular vehicle attributes. In variousembodiments, the training data may include mileage for an associatedvehicle, in addition to wear-and-tear score 1103 and depreciation data1105. In some embodiments, the training data may also include repairsdone to a vehicle, and in some embodiments, the training data may alsoinclude a monitoring data for the vehicle. In various embodiments,training data may include multiple data records 1101, with each recordprocessed consecutively by model 1020. At step 1210 of process 1200,model 1020 can acquire training data record 1101, at a step 1220 performcomputations, and at a step 1230 return a predicted depreciation valueof the already disposed of vehicle with known depreciation data 1105. Invarious embodiments, the predicted depreciation value may be comparedwith depreciation data 1105 to evaluate an associated error for model1020 at a step 1240. If the error is below the threshold value (step1240, NO), process 1200 may proceed to step 1210 of acquiring a nexttraining data record 1101. If the error is above the threshold value(step 1240, YES), process 1200 may proceed to a step 1250 of modifyingmodel parameters and subsequently returning to step 1220. In variousembodiments, model 1020 may be rated based on the average errorgenerated by model 1020. In various embodiments, a model may be tailoredfor each vehicle with given vehicle attributes.

FIG. 13 shows a process 1300 which may be a variation of process 400shown in FIG. 4. For example, process 1300 may include steps 410, 420and 430 of process 400. At a step 1350 of process 1300, a list ofsuggested maintenance actions may be generated. In some embodiments, thelist of suggested maintenance actions may include suggested repairs(e.g., transmission repair, brake replacement, body repair, headlampreplacement, etc.), suggested maintenance (e.g., oil change,transmission fluid change, etc.) suggested cleaning (interior cleaning,seat replacement, carpet cleaning, etc.) with each suggested maintenanceaction indicating expected decrease in depreciation of a vehicle (i.e.,increase in selling price of the vehicle). In various embodiments,suggested maintenance actions may be generated using machine-learningmodels. For example, a machine-learning model may include a neuralnetwork, or the like, and can be trained on data that may include awear-and-tear score for a vehicle, a predicted depreciation for thevehicle, one or more vehicle maintenance actions, and a correspondingincrease in the vehicle price due to execution of one or more of thevehicle maintenance actions.

FIG. 14 shows an exemplary graph 1400 of representative maintenanceactions labeled 1, 2 and 3 and resulting changes in a vehicle effectiveprice due to these actions. The vehicle effective price may becalculated as a difference between the vehicle sales price after themaintenance action and the cost of the corresponding maintenance action.For example, action 3 results in a vehicle effective price increasingfrom value P0 to value P1, action 1 results in the vehicle effectiveprice increasing from value P0 to value P2, and action 2 results in thevehicle effective price increasing from value P0 to value P3. If onlyone maintenance action is taken, graph 1400 shows that action 2 ispreferred to obtain the highest vehicle effective price. As shown inFIG. 13, when action 3 is followed by action 1 the vehicle may be soldat an effective price of P4, when action 1 is followed by action 2 thevehicle may be sold at an effective price of P5 that, in an illustrativeembodiment, may be larger than P4, and when action 2 is followed byaction 3 the vehicle may be sold at an effective price of P6 that may belarger than P5 in an illustrative embodiment.

In various embodiments, the order of actions may not be important andresult in expected vehicle effective price of P7 when all themaintenance action 1, 2, and 3 are taken. Graph 1300 may be presented tovehicle owner 180 via interface 194 and may allow vehicle owner 180 toquickly overview possible maintenance actions and their associatedimpact on the vehicle sales price. Graph 1400 shown in FIG. 14 is onlyillustrative, and maintenance actions may lead to various changes to thevehicle effective price. For example, in some embodiments, the order ofactions may be important. For example, the order of actions may beimportant if the last maintenance action involves cleaning the vehicle.In some embodiments, the effect of maintenance actions may depend onother factors associated with a sale of the vehicle such as the locationof the vehicle, vehicle attributes, time of sale of the vehicle, orother external factors such as fashion, weather patterns, gas prices orthe like.

In various embodiments, vehicle data system 105 may inform vehicle owner102 when may be the best time to purchase and sell vehicle 103. FIG. 15shows representative graphs of various financial data including amarginal depreciation 1510, and a marginal benefit 1520. For brevity,graphs of financial data may be referred to as curves 1510, 1520 insubsequent discussion.

As used herein, unless otherwise noted, the term “marginal depreciation”(MD) means vehicle depreciation per unit of time. For example, a dailymarginal depreciation is the change in vehicle value from one day to thefollowing day. For example, if a vehicle value is decreased by $10.45dollars in a day from the value of $26,000 of the previous day thandaily marginal depreciation is $10.45. In various embodiments, the term“depreciation” or “overall depreciation” is referred to a differencebetween the current value of vehicle 103 and the price paid for vehicle103 by owner 102. In various embodiments, depreciation may be calculatedas an area under the curve 1510. Vehicle depreciation during a timebetween a first and a second time means the loss in value of a vehicleduring that time.

As used herein, unless otherwise noted, the term “marginal benefit” (MB)is referred to the amount of money owner 102 is willing to pay per unitof time to own vehicle 103. In various embodiments, the marginal benefitmay include losses associated with vehicle repair and maintenance. Forexample, if owner 102 is willing to pay $25 per unit of time to ownvehicle 103 which may require $4 of repairs (on average) per unit oftime and $5 of maintenance per unit of time (e.g., cleaning, fees and/orinterest), the marginal benefit may be calculated as $25−$4−$5=$16.

It should be understood, that while marginal depreciation may be awell-established quantity, the marginal benefit is a highly subjectivequantity. In various embodiments, the marginal benefit may beestablished by periodically surveying owners 102. In an illustrativeembodiment, owners 102 may be asked a set of questions to evaluate themarginal benefit of their vehicles. For example, owners 102 may be askedto enter the amount of money they are willing to pay per unit of time todrive vehicle 103. In some embodiments, owners 102 may be questionedabout what vehicles they might be willing to drive. In variousembodiments, the term “benefit” or “overall benefit” is referred tobenefit received from vehicle 103 during the time owner 102 ownedvehicle 103. In various embodiments, the overall benefit may becalculated as an area under the curve 1520. Overall benefit from vehicle103 during a time between a first and a second time means the overallbenefit from vehicle 103 received by owner 103 during that time.

In various embodiments, system 105 may be configured to define socialgroups for owners 102. In various embodiments, system 105 may receive avariety of personable information from owners 102 and may be configuredto determine the social group that may include owner 102. In anillustrative embodiment, owner 102 may provide system 105 informationabout their marital status, a number of children in a family of theowner, ages of the children, occupation of owner 102, age of owner 102,car preferences for owner 102, and other suitable information that maybe used by system 105 to associate owner 102 with a social group. Invarious embodiments, a representative owner belonging to a social groupmay have the same preferences for vehicles, and, as a result, mayreceive the same (or similar) marginal benefit from their vehicles.

FIG. 15 shows that marginal depreciation 1510 may be high for a newvehicle at a time T₀, but may decrease for larger times (e.g., at timeT₂) in accordance with typical vehicle depreciation trends. For example,some vehicles may depreciate as much as 10% as soon as they are used forthe first time. In an illustrative embodiment shown in FIG. 15, marginalbenefit 1520 enjoyed by owning a vehicle may be steady and high when avehicle is new (e.g., at time T₀ as shown in FIG. 15) but may decreaseconsiderably for an older vehicle (e.g., at time T₂, as shown in FIG.15). The decrease in marginal benefit 1520 is related to overall ownerdissatisfaction with the aging vehicle 103 either because of variousdefects incurred by the vehicle 103 throughout ownership time, or theavailability of newer, more attractive vehicles. In some cases,financially burdensome increasing maintenance costs for older vehicle103 may decrease marginal benefit from vehicle 103. In variousembodiments, marginal benefit 1520 obtained from vehicle 103 may includevarious losses associated with owning or leasing vehicle 103 (e.g.,costs associated with parking vehicle 103, as well as taxes and interestpaid by owner, toll fees, gas fees, etc.)

For brevity of discussion, FIG. 15 uses time as a parameter for plottingmarginal data, such as curves 1510 and 1520, with the understanding thatmiles driven by vehicle 103 may be used in a similar way. For thepurposes of this discussion, all the elements, concepts and notationrelated to time may be equally applied to miles driven by vehicle 103.

FIG. 15 shows a loss region 1501 that may be calculated as a differencebetween marginal benefit curve 1520 and marginal depreciation curve1510. In various embodiments, a loss is characterized by a negativenumber. For example, for vehicle 103 with the marginal benefit of $30per day (i.e., user is willing to pay $30 per day to drive vehicle 103)and the marginal depreciation of $500 a day (marginal depreciation maybe very high for new vehicles), the loss may be $30−$500=−$470. Invarious embodiments, loss 1501 associated with large marginaldepreciation 1510 may be avoided by buying a used vehicle at a time T₁when curve 1510 intersects curve 1520.

When marginal benefit 1520 is higher than marginal depreciation 1510,owner 102 may experience gain 1502. After a time T₂, when curve 1510intersects curve 1520, marginal depreciation 1510 becomes higher thanmarginal benefit 1520 and owner 102 may experience overall loss 1503. Inorder to avoid loss 1503, owner 102 may sell vehicle 103 at time T₂. Invarious embodiments, owner 102 may place vehicle 103 for sale prior totime T₂ in order to account for the time needed to sell the vehicle. Inan example embodiment, system 105 may recommend owner 102 to sellvehicle 103 at a time T₃ that is prior and in the proximity of time T₂.In an example embodiment, time T₃ may be determined when the differencebetween curve 1520 and curve 1510 is below a target value selected bysystem 105 and when the difference is decreasing.

In various embodiments, system 105 may display curves 1510 and 1520 forowner 102 at past, current and future times. For example, system 105 mayextrapolate marginal depreciation curve 1510 for future times based onknown current and past data for vehicle 103, as well as data forvehicles with the same vehicle attributes stored in database 120 ofsystem 105. Similarly, system 105 may extrapolate marginal benefit curve1520 based on various known trends in the marginal benefit for owners102.

Curves 1510 and 1520 may be one example of marginal depreciation andmarginal benefit. FIG. 16 illustrates similar curves for two differentvehicles. For example, curve labeled MD V1 may correspond to a marginaldepreciation of a first vehicle, curve labeled MD V2 may correspond to amarginal depreciation of a second vehicle, curve MB V1 may correspond toa marginal benefit of a first vehicle, and curve MB V2 may correspond toa marginal benefit of a second vehicle. In various embodiments, arealabeled G1 may correspond to gain to owner 102 from having the firstvehicle and area labeled G2 may correspond to gain to owner 102 fromhaving the second vehicle. FIG. 16 shows that while the second vehiclemay have a higher overall price and higher initial depreciation rates,gain G2 is higher than gain G1 due to marginal benefit of choosing asecond vehicle over a first vehicle. In an illustrated embodiment, thefirst vehicle may be a family sedan with basic features, while thesecond vehicle may be a fine sports car with a powerful engine andrefined trim line. For a young owner 102, the second car may offersubstantially more benefit in spite of the second car's higher upfrontcost, higher maintenance cost, and higher depreciation rates.

In various embodiments, system 105 may suggest options to owner 102based on owner preferences. In some embodiments, for example, system 105may present owner 102 with marginal depreciation and marginal benefitcurves for various vehicles via interface system 150. For example,charts depicted in FIGS. 15-16 may be plotted for the owner 102 usinginterface system 150. In various embodiments, charts displayed byinterface system 150 may allow owner 102 to select the best vehicleoption as well as the best times to buy and sell vehicle 103. In someembodiments, system 105 may suggest owner 102 to consider owning vehicle103 for a selected period of time when owner 102 may be experiencinggain from owning vehicle 103. For example, FIG. 17 shows a gain G3 for avehicle V1 and a gain G4 for a vehicle V2. Owner 102 may first ownvehicle V1 for a time T₄ and then vehicle V2 for a time T₅ resulting inoverall benefits higher that might have been obtained from owning onecar for a time T₆ than may be longer than time T₄ or time T₅ (forexample, time T₆ may be comparable to T₄+T₅).

While FIGS. 15-17 demonstrate one approach for estimating the time (ormiles traveled by a vehicle) for selling the vehicle, various otherapproaches may be used as well. For example, as shown in FIG. 18 thedisposal time may be influenced by a significant change in depreciationfor the vehicle that may be based on historical data. In an illustrativeembodiment, the vehicle may be disposed of or repaired if a change invehicle depreciation is predicted. For example, a significant change indepreciation may be predicted based on previously known depreciationtrends for vehicle 103. In an example embodiment, vehicle 103 may beknown to have mechanical problems that manifest themselves for vehiclesthat are older than a threshold age or have a millage above a thresholdvalue.

In various embodiments, a statistical approach may be used to determinewhen to dispose of vehicle 103 using only depreciation data for variousvehicles with the same vehicle attributes (i.e., vehicle year, make,model, mileage and trim line). FIG. 19 shows an illustrative embodiment,of distributions 1901 and 1902 of a number of cars sold as a function oftime (age of vehicle 103) or mileage traveled by vehicle 103. In variousembodiments, distributions 1901 and 1902 may be evaluated for non-ownedvehicles that have the same vehicle attributes as vehicle 103 owned byowner 102. In some embodiments, distribution 1902 may correspond to thenon-owned vehicles with low wear-and-tear score, and distribution 1901may correspond to the non-owned vehicles with high wear-and-tear score.For example, low wear-and-tear score may be in the bottom ten percentrange of wear-and-tear scores recorded by system 105, and highwear-and-tear score may be in the top ten percent range of wear-and-tearscores. It should be noted that specific numbers describing low and highwear-and-tear scores are only illustrative and any other numbers may bechosen as convenient. For example, curves 1901 and 1902 may shift andchange shape depending on corresponding wear-and-tear score related tothese curves.

In various embodiments, curves 1901 and 1902 may exhibit maximum attimes T_(s1) and T_(s2) indicating the best times to sell a vehicle witha corresponding wear-and-tear score. In various embodiments, system 105may plot wear-and-tear score for owner 102 as a function of time. In anillustrated embodiment shown in FIG. 19, owner 102 may have awear-and-tear score 1925 that is somewhere between a maximumwear-and-tear score 1922 and a minimum wear-and-tear score 1921, wherewear-and-tear scores 1921, 1922, and 1925 are plotted as a function oftime. In an illustrative embodiment, since wear-and-tear score 1925 issomewhere between wear-and-tear score 1921 and 1922, it may be best tosell vehicle 103 at a time T_(s) that may be between T_(s1) and T_(s2).

In an illustrative embodiment, if the difference between wear-and-tearscore 1925 and maximum wear-and-tear score 1922 is R₂ and the differencebetween wear-and-tear score 1925 and minimum wear-and-tear score 1921 isR₁, then time for selling vehicle 103 may be evaluated asT_(s)=T_(s1)+[R₂/(R₁+R₂)](T_(s2)−T_(s1)). For example, for wear-and-tearscore 1925 matching low wear-and-tear score 1921, R₁=0, andT_(s)=T_(s2). Alternatively, for wear-and-tear score 1925 matching highwear-and-tear score 1922, R₂=0, and T_(s)=T_(s1). It should be notedthat expression for T_(s) corresponds to a linear interpolation based onvalues of R₁ and R₂ at time T_(s1). In various embodiments, values of R₁and R₂ may change as a function of time, and other expressions may beused to obtain an appropriate prediction for selling vehicle 103.

In various embodiments, vehicle owner 102 may monitor a wear-and-tearscore for the vehicle via interface 130. In some embodiment, vehicleowner 102 may monitor a comprehensive (single) wear and tear score, andin some embodiments, vehicle owner 102 may monitor a wear and tear scoreassociated with individual components of the vehicle. In someembodiments, system 105 may be configured to inform vehicle owner 102about rapid changes in the wear and tear score that may indicate thatthe vehicle needs to be disposed of or repaired. For example, the wearand tear score may be plotted as a function of time as shown in FIG. 20,indicating a vehicle failure. In some embodiments, the rate of change ofcomprehensive or individual wear and tear score above a threshold valuemay result in system 105 issuing a notification to vehicle owner 102. Invarious embodiments, threshold values may be determined based on ahistorical correlation between various wear and tear scores and variousvehicle failures associated with those scores.

In various embodiments, various financial data may be used by vehiclesystem 105 for obtaining a recommendation for vehicle disposal forvehicle owner 102. In an illustrative embodiment, vehicle depreciationmay be used, and additionally or alternatively, vehicle marginaldepreciation may be used as described above. In various embodiments,vehicle depreciation or vehicle marginal depreciation may be a part ofdepreciation data obtained by vehicle owner system, and various elementsof this depreciation data may be used for predicting the best time forvehicle disposal.

In various embodiments, an overall benefit generated by a vehicle may beused, and additionally or alternatively, vehicle marginal benefit may beused as described above. In various embodiments, vehicle overall benefitor vehicle marginal benefit may be a part of benefit data obtained bysystem 105, and various elements of this benefit data may be used forpredicting the best time for vehicle disposal. In various embodiments, again from a vehicle may include a difference between overall benefitgenerated by a vehicle and overall vehicle depreciation. In variousembodiments, the negative gain may be referred to as loss.

The foregoing description has been presented for purposes ofillustration. It is not exhaustive and is not limited to precise formsor embodiments disclosed. Modifications and adaptations of theembodiments will be apparent from a consideration of the specificationand practice of the disclosed embodiments. For example, while certaincomponents have been described as being coupled to one another, suchcomponents may be integrated with one another or distributed in anysuitable fashion.

Moreover, while illustrative embodiments have been described herein, thescope includes any and all embodiments having equivalent elements,modifications, omissions, combinations (e.g., of aspects across variousembodiments), adaptations and/or alterations based on the presentdisclosure. The elements in the claims are to be interpreted broadlybased on the language employed in the claims and not limited to examplesdescribed in the present specification or during the prosecution of theapplication, which examples are to be construed as non-exclusive.Further, the steps of the disclosed methods can be modified in anymanner, including reordering steps and/or inserting or deleting steps.

The features and advantages of the disclosure are apparent from thedetailed specification, and thus, it is intended that the appendedclaims cover all systems and methods falling within the true spirit andscope of the disclosure. As used herein, the indefinite articles “a” and“an” mean “one or more.” Similarly, the use of a plural term does notnecessarily denote a plurality unless it is unambiguous in the givencontext. Words such as “and” or “or” mean “and/or” unless specificallydirected otherwise. Further, since numerous modifications and variationswill readily occur from studying the present disclosure, it is notdesired to limit the disclosure to the exact construction and operationillustrated and described, and accordingly, all suitable modificationsand equivalents may be resorted to, falling within the scope of thedisclosure.

Other embodiments will be apparent from a consideration of thespecification and practice of the embodiments disclosed herein. It isintended that the specification and examples be considered as an exampleonly, with a true scope and spirit of the disclosed embodiments beingindicated by the following claims.

What is claimed is:
 1. A system for providing a disposal recommendationfor an owned vehicle, the system comprising one or more memory devicesstoring instructions and one or more processors executing theinstructions to perform operations comprising: generating a profile forthe owned vehicle, the profile comprising age, mileage, and location ofthe owned vehicle; receiving monitoring data of the owned vehicle, themonitoring data being collected by sensors and comprising at least oneof acceleration data, on-board diagnostics data, a history of locationdata for the owned vehicle, a history of weather data, a speed, or arepair data for the owned vehicle; receiving sales data for non-ownedvehicles corresponding to the profile; generating predicted depreciationdata of the owned vehicle, based on the sales data and the monitoringdata via a statistical approach; and providing a recommended disposaltime for the owned vehicle, based on the predicted depreciation data,wherein providing a recommended disposal time comprises: establishing atarget depreciation value; and providing a recommended disposal time forthe owned vehicle when the owned vehicle predicted depreciation value islower than the target depreciation value.
 2. The system of claim 1,further comprising receiving repair data for non-owned vehiclescorresponding to the profile, wherein providing the recommended disposaltime comprises providing the recommended disposal time, based on thepredicted depreciation values and the repair data.
 3. The system ofclaim 2, wherein the repair data comprises a type of a repair, a laborcost of the repair, and a parts cost of the repair.
 4. The system ofclaim 1, wherein the monitoring data comprises a history of weather datacorresponding to the history of the location data for the owned vehicle.5. The system of claim 1, wherein the operations further comprisegenerating a wear-and-tear score for the owned vehicle based on themonitoring data.
 6. The system of claim 5, wherein the operationsfurther comprise receiving wear-and-tear scores for the non-ownedvehicles.
 7. The system of claim 6, wherein the operations furthercomprise updating the predicted depreciation data by comparing thewear-and-tear score for the owned vehicle to the wear-and-tear scoredfor the non-owned vehicles.
 8. The system of claim 5, wherein theoperations further comprise providing the wear-and-tear score for theowned vehicle.
 9. The system of claim 8, wherein the operations furthercomprise providing the wear-and-tear score for the non-owned vehicle.10. The system of claim 1, wherein the statistical approach includes alinear regression.
 11. A system for providing a disposal recommendationfor an owned vehicle, the system comprising: a database configured to:store a profile for the owned vehicle, the owned vehicle profilecomprising age, mileage, and location of the first vehicle; storemonitoring data for the owned vehicle, the monitoring data beingcollected by sensors and comprising at least one of acceleration data,on-board diagnostics data, a history of location data for the ownedvehicle, a history of weather data, a speed, or a repair data for theowned vehicle; and store sales data for non-owned vehicles correspondingto the profile; one or more memory devices storing instructions; and oneor more processors executing the instructions to perform operationscomprising: generating predicted depreciation data of the owned vehicle,based on the sales data and the monitoring data via a statisticalapproach; and providing a recommended disposal time for the ownedvehicle, based on the predicted depreciation data, wherein providing arecommended disposal time comprises: evaluating a marginal depreciationvalue; evaluating a marginal benefit value; and providing a recommendeddisposal time for the owned vehicle when the difference between themarginal benefit value and the marginal depreciation value is betweenzero and a target value.
 12. The system of claim 11, wherein: thedatabase is further configured to store repair data for non-ownedvehicles corresponding to the profile; and the operations furthercomprise providing the recommended disposal time based on the predictedrepair data.
 13. The system of claim 11, wherein the repair datacomprises a type of a repair, a labor cost of the repair, and a partscost of the repair.
 14. The system of claim 11, wherein the operationsfurther comprise generating a wear-and-tear score for the owned vehiclebased on the monitoring data.
 15. The system of claim 14, wherein theoperations further comprise receiving wear-and-tear scores for thenon-owned vehicles.
 16. A system for providing a disposal recommendationfor an owned vehicle, the system comprising one or more memory devicesstoring instructions and one or more processors executing theinstructions to perform operations comprising: generating a profile forthe owned vehicle, the profile comprising age, mileage, and location ofthe owned vehicle; receiving monitoring data of the owned vehicle, themonitoring data being collected by sensors and comprising at least oneof acceleration data, on-board diagnostics data, a history of locationdata for the owned vehicle, a history of weather data, a speed, or arepair data for the owned vehicle; receiving sales data for non-ownedvehicles corresponding to the profile; generating predicted depreciationdata of the owned vehicle, based on the sales data and the monitoringdata via a statistical approach; and providing a recommended disposaltime for the owned vehicle, based on the predicted depreciation data,wherein providing a recommended disposal time comprises: evaluating amarginal depreciation value at future times; evaluating a marginalbenefit value at future times; and providing a recommended disposalfuture time for the owned vehicle when the marginal benefit value andthe marginal depreciation value are equal to each other.
 17. The systemof claim 16, further comprising receiving repair data for non-ownedvehicles corresponding to the profile, wherein providing the recommendeddisposal time comprises providing the recommended disposal time, basedon the predicted depreciation values and the repair data.
 18. The systemof claim 16, wherein the repair data comprises a type of a repair, alabor cost of the repair, and a parts cost of the repair.
 19. The systemof claim 16, wherein the monitoring data comprises a history of weatherdata corresponding to the history of the location data for the ownedvehicle.